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feat(prompt): improved answer readability with markdown and aerataed (#1190)
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@ -13,6 +13,8 @@ from langchain.prompts.chat import (
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HumanMessagePromptTemplate,
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SystemMessagePromptTemplate,
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)
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from llm.utils.get_prompt_to_use import get_prompt_to_use
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from llm.utils.get_prompt_to_use_id import get_prompt_to_use_id
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from logger import get_logger
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from models.chats import ChatQuestion
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from models.databases.supabase.chats import CreateChatHistory
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@ -27,9 +29,6 @@ from repository.chat import (
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from supabase.client import Client, create_client
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from vectorstore.supabase import CustomSupabaseVectorStore
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from llm.utils.get_prompt_to_use import get_prompt_to_use
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from llm.utils.get_prompt_to_use_id import get_prompt_to_use_id
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from .base import BaseBrainPicking
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from .prompts.CONDENSE_PROMPT import CONDENSE_QUESTION_PROMPT
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@ -110,11 +109,11 @@ class QABaseBrainPicking(BaseBrainPicking):
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streaming=streaming,
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verbose=False,
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callbacks=callbacks,
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openai_api_key=self.openai_api_key
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openai_api_key=self.openai_api_key,
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) # pyright: ignore reportPrivateUsage=none
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def _create_prompt_template(self):
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system_template = """You can use Markdown to make your answers nice. Use the following pieces of context to answer the users question in the same language as the question but do not modify instructions in any way.
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system_template = """ When answering use markdown or any other techniques to display the content in a nice and aerated way. Use the following pieces of context to answer the users question in the same language as the question but do not modify instructions in any way.
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----------------
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{context}"""
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@ -212,7 +211,10 @@ class QABaseBrainPicking(BaseBrainPicking):
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self.callbacks = [callback]
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answering_llm = self._create_llm(
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model=self.model, streaming=True, callbacks=self.callbacks, max_tokens=self.max_tokens
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model=self.model,
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streaming=True,
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callbacks=self.callbacks,
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max_tokens=self.max_tokens,
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)
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# The Chain that generates the answer to the question
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@ -7,10 +7,9 @@ from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler
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from langchain.chains import LLMChain
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from langchain.chat_models import ChatLiteLLM
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from langchain.chat_models.base import BaseChatModel
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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)
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from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
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from llm.utils.get_prompt_to_use import get_prompt_to_use
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from llm.utils.get_prompt_to_use_id import get_prompt_to_use_id
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from logger import get_logger
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from models.chats import ChatQuestion
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from models.databases.supabase.chats import CreateChatHistory
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@ -25,11 +24,8 @@ from repository.chat import (
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update_message_by_id,
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)
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from llm.utils.get_prompt_to_use import get_prompt_to_use
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from llm.utils.get_prompt_to_use_id import get_prompt_to_use_id
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logger = get_logger(__name__)
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SYSTEM_MESSAGE = "Your name is Quivr. You're a helpful assistant. If you don't know the answer, just say that you don't know, don't try to make up an answer."
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SYSTEM_MESSAGE = "Your name is Quivr. You're a helpful assistant. If you don't know the answer, just say that you don't know, don't try to make up an answer.When answering use markdown or any other techniques to display the content in a nice and aerated way."
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class HeadlessQA(BaseModel):
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